A class of parameter estimation methods for nonlinear Muskingum model using hybrid invasive weed optimization algorithm
type: Item
instance of: scholarly article
title: A class of parameter estimation methods for nonlinear Muskingum model using hybrid invasive weed optimization algorithm
zbMATH Open document ID: 1394.90575
DOI: 10.1155/2015/573894
author: Aijia Ouyang
author: Libin Liu
author: Zhou Sheng
author: Fan Wu
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameter \(\theta\) to approximate the nonlinear Muskingum model based on general trapezoid formulas. The accuracy of these schemes is second order, if \(\theta \neq 1 / 3\), but interestingly when \(\theta = 1 / 3\), the accuracy of the presented scheme gets improved to third order. Then, the present schemes are transformed into an unconstrained optimization problem which can be solved by a hybrid invasive weed optimization (HIWO) algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the present methods. The numerical results substantiate the fact that the presented methods have better precision in estimating the parameters of nonlinear Muskingum models.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 62P25
Mathematics Subject Classification ID: 65K05
zbMATH DE Number: 6926680
Wikidata QID: Q59119389
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/573894
OpenAlex ID: W2110305691
cites work: A differential invasive weed optimization algorithm for improved global numerical optimization
cites work: Generalized trapezoidal formulas for parabolic equations
cites work: A novel parameter estimation method for muskingum model using new Newton-type trust region algorithm
cites work: A Simplex Method for Function Minimization
description: scientific article; zbMATH DE number 6926680